A data-driven method for IGBT open-circuit fault diagnosis of NPC inverters in three-phase photovoltaic grid-connected systems

Abstract Data-driven methods have exhibited excellent performance and promising prospects in the fault diagnosis of power electronics systems. This article proposed an open-circuit fault diagnosis method based on locally linear embedding (LLE) and regularized extreme learning machine (RELM) for insulated gate bipolar transistor (IGBT) single-tube open-circuit faults of three-level neutral point clamped (NPC) inverters in three-phase photovoltaic grid-connected power generation systems. By building a simulation model of the photovoltaic power generation system, the A-phase output current sample data of IGBT single-tube open-circuit faults under different light intensities are obtained. Subsequently, the sample data is processed by dimensionality reduction and used for the training of the fault diagnosis model. Through optimizing the dimensionality of LLE reduction and the number of hidden layer nodes in the RELM, the model attains optimal diagnostic speed and accuracy. Finally, comparative experiments with other machine learning algorithms demonstrated that the method proposed in this article effectively improved the performance of the IGBT single-tube open-circuit fault diagnosis system for three-level photovoltaic NPC inverters.